Edinburgh bin sensor project - analysis report
Introduction
This notebook plays around with some visualisation of the total weight/volume of the rubbish for the streets with bin sensors.
Load in the clean data:
library(tidyverse)
library(sf)
library(scales)
#read in the cleaned and combined bin & osm data
combined_bin_osm <- st_read(here::here("cleaned_data/combined_bin_osm_data/combined_bin_osm.shp"), quiet = TRUE)
#undoing some shortened naming when read to shape file
combined_bin_osm <- combined_bin_osm %>%
rename("street_name" = "strt_nm",
"total_vol_l3" = "ttl_v_3",
"total_weight_kg" = "ttl_wg_",
"cumul_total_vol_l3" = "cm_t__3",
"cumul_total_weight_kg" = "cm_tt__",
"highway_group" = "hghwy_g")
# read in all the .shp files in the cleaned osm data folder
root <- here::here()
dir_path <- paste0(root, '/cleaned_data/osm_data/')
file_pattern <- '*.shp'
shp_files <- list.files(dir_path, pattern = file_pattern)
for (i in seq_along(shp_files)) {
assign(str_remove(shp_files[i], ".shp"), st_read(paste0(dir_path, shp_files[i]), quiet = TRUE))
}
#undoing some shortened naming when read to shape file
streets_simplified <- streets_simplified %>%
rename("street_name" = "strt_nm",
"highway_group" = "hghwy_g")Cumulative total rubbish
Going to look at the cumulative total rubbish. Start by subsetting the data to get the final bin collection date amount for each street:
# subset the data so only the last bin collection date (so get 1 row per street)
last_date <- combined_bin_osm %>%
st_drop_geometry() %>% #no need for geometry
summarise(min_date = max(date))%>%
pull()
last_date_bin_collection_sf <- combined_bin_osm %>%
filter(date == last_date)Visualising the top and bottom streets for total rubbish weight
last_date_bin_collection_sf %>%
slice_max(order_by = cumul_total_weight_kg, n = 5) %>%
ggplot(aes(x = reorder(str_to_title(street_name), -cumul_total_weight_kg) , y = cumul_total_weight_kg)) +
geom_col(fill = "slategray3") +
scale_y_continuous(labels = scales::comma) +
theme_minimal() +
labs(x = "Location", y = "Cumulative total rubbish weight (kg)", title = "Streets with largest cumulative rubbish weight over 67 days") 
last_date_bin_collection_sf %>%
slice_min(order_by = cumul_total_weight_kg, n = 5) %>%
ggplot(aes(x = reorder(str_to_title(street_name), cumul_total_weight_kg) , y = cumul_total_weight_kg)) +
geom_col(fill = "slategray3") +
theme_minimal() +
#ylim(c(0, max(last_date_bin_collection_sf$cumul_total_weight_kg))) +
labs(x = "Location", y = "Cumulative total rubbish weight (kg)", title = "Streets with lowest cumulative rubbish weight over 67 days")
It can be difficult to think about the weights i.e. how heavy is 20,000 kg, so have also produced these plots with some contextual weights:
| Object | Average weight (kg) |
|---|---|
| Baby elephant | 120 |
| Vauxhall Corsa | 980 |
| Adult elephant | 4,300 |
| UK bus | 12,000 |
| 18 wheeler truck | 36,000 |
last_date_bin_collection_sf %>%
slice_max(order_by = cumul_total_weight_kg, n = 5) %>%
ggplot(aes(x = reorder(str_to_title(street_name), -cumul_total_weight_kg) , y = cumul_total_weight_kg)) +
geom_col(fill = "slategray3") +
scale_y_continuous(breaks = c(120, 980, 4300,12000, 36000) , labels = c('baby elephant', 'vauxhall corsa', 'adult elephant', 'bus', '18 wheeler truck'), limits = c(0,40000)) +
theme_minimal() +
labs(x = "Location", y = "Cumulative total rubbish weight", title = "Streets with largest cumulative rubbish weight") 
Visualsing spatially the total rubbish weight per street
Setting up the ‘base’ spatial plot of Edinburgh city centre using the OSM data. The line thickness denotes the type of street/road. The wider the thickness the more ‘major’ the street/road is (either small/medium/large).
# Plots -------------------------------------
# setting the max and min lat/long for cropping visual
min_max_coords <- c(ymin= 55.928479867725436,
xmin= -3.227624857214751,
ymax= 55.98333902701634,
xmax= -3.140735628435386)
map_theme <-theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
plot.background=element_blank(),
panel.grid.minor=element_blank(),
panel.background=element_blank(),
panel.grid.major=element_blank())
base_plot <- ggplot() +
map_theme +
geom_sf(data = water,
fill = "steelblue",
# size = .8,
lwd = 0,
alpha = .3) +
geom_sf(data = park_multipoly,
fill = "green",
# size = .8,
lwd = 0,
alpha = .3) +
geom_sf(data = park_poly,
fill = "green",
# size = .8,
lwd = 0,
alpha = .3) +
geom_sf(data = railways,
color = "grey30",
size = .2,
linetype="dotdash",
alpha = .5) +
geom_sf(data = filter(streets_simplified, highway_group == "small"),
size = .1,
color = "grey40") +
geom_sf(data = filter(streets_simplified, highway_group == "medium"),
size = .3,
color = "grey35") +
geom_sf(data = filter(streets_simplified, highway_group == "large"),
size = .5,
color = "grey30") +
coord_sf(ylim = c(min_max_coords[1], min_max_coords[3]),
xlim = c(min_max_coords[2], min_max_coords[4]),
expand = FALSE)
base_plot +
labs(caption = 'Edinburgh - base plot using OpenStreetMap data', size = 2) 
Now playing around with different ways to visualise the data.
Highlighting which streets have sensor bins on:
# Highlighting which streets have sensor bins on ----------------------------
# to have in README
base_plot +
geom_sf(data = last_date_bin_collection_sf, aes(color = "street with sensor bin"), size = .7, show.legend = "line") +
labs(caption = 'Edinburgh - bin sensor project', title= "Plot of which streets have bin sensors", size = 2) +
coord_sf(ylim = c(min_max_coords[1], min_max_coords[3]),
xlim = c(min_max_coords[2], min_max_coords[4]),
expand = FALSE) +
theme(legend.position = "top", legend.title = element_blank())## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
#ggsave("plot_images/streets_highlighted.png")A lot of sequential colour palettes begin at very light colours which would make it hard to see some of the streets with lower levels of rubbish, tested out a few colour schemes:
# Last bin collection colour showing weight ----------------------------
base_plot +
geom_sf(data = last_date_bin_collection_sf, aes(colour = cumul_total_weight_kg),
size = 1,
show.legend = "line") +
scale_colour_gradient(low = "yellow", high = "red", name = "Cumulative total \nrubbish weight (kg)") +
labs(caption = 'Edinburgh - bin sensor project', title = "Plot of cumulative rubbish weight over 67 days for bin sensor streets", size = 2) +
coord_sf(ylim = c(min_max_coords[1], min_max_coords[3]),
xlim = c(min_max_coords[2], min_max_coords[4]),
expand = FALSE) ## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
#ggsave("plot_images/streets_by_weight_red_yellow.png")From this blog here by Lisa Charlotte Rost she said on sequential colour schemes ‘Using two or even more hues increases the color contrast between segments of your gradient, making it easier for readers to distinguish between them’ so came across some on CARTO so gave one a go:
library(rcartocolor)
base_plot +
geom_sf(data = last_date_bin_collection_sf, aes(colour = cumul_total_weight_kg),
size = 1,
show.legend = "line") +
scale_color_carto_c(palette = "SunsetDark", name = "Cumulative total \nrubbish weight (kg)") +
#name = "Cumulative total \nrubbish weight (kg)") +
labs(caption = 'Edinburgh - bin sensor project', title = "Plot of cumulative rubbish weight over 67 days for bin sensor streets", size = 2) +
coord_sf(ylim = c(min_max_coords[1], min_max_coords[3]),
xlim = c(min_max_coords[2], min_max_coords[4]),
expand = FALSE) ## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
#ggsave("plot_images/streets_by_weight_sunset.png")In future want to give this more of an investigation and look into some more palettes (or potentially using a different coloured background).
Princes Street is a bit of an outlier here so hard to see the differences between the other streets.
last_date_bin_collection_sf %>%
ggplot(aes(cumul_total_weight_kg)) +
geom_boxplot() +
xlab("Cumulative total rubbish weight (kg)") +
ggtitle("Boxplot of cumulative rubbish weight over 67 days for bin sensor streets") +
theme_minimal() +
scale_x_continuous(labels = scales::comma) +
theme(axis.text.y=element_blank(), axis.ticks.y=element_blank())
Yes Princes Street is a big outlier. Makes sense as it is the main shopping street in Edinburgh and lots of footfall. In future look to see if any open footfall data to add to analysis. Could do a transformation of the variable to show the differences between the other streets more, or show same plot without Princes Street (which have done below).
# Last bin collection colour showing weight removing Princes Street as is ----------------------------
base_plot +
geom_sf(data = filter(last_date_bin_collection_sf, street_name != "princes street"), aes(colour = cumul_total_weight_kg),
size = 1,
show.legend = "line") +
scale_colour_gradient(low = "yellow", high = "red", name = "Cumulative total \nrubbish weight (kg)") +
labs(caption = 'Edinburgh - bin sensor project', title = "Plot of cumulative rubbish weight over 67 days for bin sensor streets\n(omitting Princes Steet)", size = 2) +
coord_sf(ylim = c(min_max_coords[1], min_max_coords[3]),
xlim = c(min_max_coords[2], min_max_coords[4]),
expand = FALSE)## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
#ggsave("plot_images/streets_by_weight_red_yellow_nops.png")Play around with using the using the thickness of the street to denote the weight:
# Last bin collection thickness showing weight ----------------------------
# decided against this way to visualise as not very clear
base_plot +
geom_sf(data = last_date_bin_collection_sf, aes(size = cumul_total_weight_kg),
show.legend = "line", colour = "red") +
labs(caption = 'Edinburgh - bin sensor project', size = "Cumulative total \nrubbish weight (kg)", title = "Plot of cumulative rubbish weight over 67 days for bin sensor streets") +
coord_sf(ylim = c(min_max_coords[1], min_max_coords[3]),
xlim = c(min_max_coords[2], min_max_coords[4]),
expand = FALSE) ## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Don’t think this is a good way of visualising this data!
Visalising the cumulative rubbish on each street over time
Now going to look at how the weight changes over time:
# Streets over time ----------------------------
combined_bin_osm %>%
ggplot(aes(x = date, y = cumul_total_weight_kg, group = street_name, colour = street_name == "princes street")) +
geom_line() +
scale_colour_manual(values = c("grey40", "red"), guide = "none") +
annotate("text", x=as.Date("2016-07-30"), y=27000, label="Princes Street", color = "red") +
theme_minimal() +
scale_x_date(date_labels = "%d%b%y") +
labs(x = "Date", y = "Cumulative total rubbish weight (kg)", title = "Cumulative rubbish weight by street") +
scale_y_continuous(labels = scales::comma)
#ggsave("plot_images/weight_over_time.png")Again, Princes Street dominates the chart here but it has a steady rate of change. Going to look more at the rate of change for each of the streets by visualising the log transformations.
#transform to a log axis on y to compare rate of change
combined_bin_osm %>%
ggplot(aes(x = date, y = cumul_total_weight_kg, group = street_name)) +
geom_line() +
scale_y_continuous(trans = "log10") +
scale_x_date(date_labels = "%d%b%y") +
theme_minimal() +
labs(y = "Log transform of cumulative weight of rubbish in kg in" , x = "Date", title = "Log transform of cumulative rubbish weight (kg) by street")## Warning: Transformation introduced infinite values in continuous y-axis
Seems to be a common pattern for most of the streets in the rate of change. Pinpointed 2 locations, Calton Hill and Hermitage Place, that seem to have a bit of a different pattern to look at in isolation:
combined_bin_osm %>%
filter(street_name %in% c("calton hill", "hermitage place")) %>%
ggplot(aes(x = date, y = cumul_total_weight_kg, color = street_name)) +
geom_line() +
scale_y_continuous(trans ="log10") +
scale_x_date(date_labels = "%d%b%y") +
labs(y = "Log transform of cumulative weight of rubbish in kg", x = "Date", title = "Log transform of cumulative rubbish weight (kg) by street", colour = "Street name") +
theme_minimal() ## Warning: Transformation introduced infinite values in continuous y-axis
combined_bin_osm %>%
filter(street_name %in% c("calton hill", "hermitage place")) %>%
ggplot(aes(x = date, y = cumul_total_weight_kg, color = street_name)) +
scale_x_date(date_labels = "%d%b%y") +
labs(y = "Cumulative weight of rubbish in kg", x = "Date", title = "Cumulative rubbish weight (kg) by street", colour = "Street name") +
geom_line() +
theme_minimal() +
scale_y_continuous(labels = scales::comma)
#ggsave("plot_images/weight_over_time_subset.png")combined_bin_osm %>%
filter(street_name == "hermitage place") %>%
st_drop_geometry() %>%
select(street_name, date, total_weight_kg, cumul_total_weight_kg) %>%
head(5)For Hermitage Place there was only a small bit of rubbish on the 2nd day of having the sensor (it is a little out of city centre) or the sensor perhaps didn’t work after this day. Similarly for Calton Hill perhaps there was an issue with the sensor as after the start of July the rate of increase increases sharply.